| 2026 | SIGIR | Unified Semantic Modeling Framework for Large-Scale Job Understanding at LinkedIn. | Dan Xu, Baofen Zheng, Jianqiang Shen, Qi Xiao, Benjamin Hoan Le, Wen Pu, Saurabh Gupta, Ran Zhou, Neha Saraf, Alice Leung, Qianqi Shen, Liangjie Hong, Jingwei Wu, Wenjing Zhang |
| 2025 | CIKM | Powering Job Search at Scale: LLM-Enhanced Query Understanding in Job Matching Systems. | Ping Liu, Jianqiang Shen, Qianqi Shen, Chunnan Yao, Kevin Kao, Dan Xu, Rajat Arora, Baofen Zheng, Caleb Johnson, Liangjie Hong, Jingwei Wu, Wenjing Zhang |
| 2025 | EMNLP | CoRAG: Enhancing Hybrid Retrieval-Augmented Generation through a Cooperative Retriever Architecture. | Zaiyi Zheng, Song Wang, Zihan Chen, Yaochen Zhu, Yinhan He, Liangjie Hong, Qi Guo, Jundong Li |
| 2025 | ICLR | Causal Effect Estimation with Mixed Latent Confounders and Post-treatment Variables. | Yaochen Zhu, Jing Ma, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li |
| 2025 | KDD | A Scalable and Efficient Signal Integration System for Job Matching. | Ping Liu, Rajat Arora, Xiao Shi, Benjamin Le, Qianqi Shen, Jianqiang Shen, Chengming Jiang, Nikita Zhiltsov, Priya Bannur, Yidan Zhu, Liming Dong, Haichao Wei, Qi Guo, Luke Simon, Liangjie Hong, Wenjing Zhang |
| 2025 | RecSys | Scaling Retrieval for Web-Scale Recommenders: Lessons from Inverted Indexes to Embedding Search. | Yuchin Juan, Jianqiang Shen, Shaobo Zhang, Qianqi Shen, Caleb Johnson, Luke Simon, Liangjie Hong, Wenjing Zhang |
| 2024 | CIKM | Learning Links for Adaptable and Explainable Retrieval. | Jianqiang Shen, Yuchin Juan, Ping Liu, Wen Pu, Shaobo Zhang, Qianqi Shen, Liangjie Hong, Wenjing Zhang |
| 2024 | CIKM | Understanding and Modeling Job Marketplace with Pretrained Language Models. | Yaochen Zhu, Liang Wu, Binchi Zhang, Song Wang, Qi Guo, Liangjie Hong, Luke Simon, Jundong Li |
| 2024 | WWW | Collaborative Large Language Model for Recommender Systems. | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li |
| 2023 | KDD | Path-Specific Counterfactual Fairness for Recommender Systems. | Yaochen Zhu, Jing Ma, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li |
| 2022 | KDD | Decision Intelligence and Analytics for Online Marketplaces: Jobs, Ridesharing, Retail and Beyond. | Zhiwei (Tony) Qin, Liangjie Hong, Rui Song, Hongtu Zhu, Mohammed Korayem, Haiyan Luo, Michael I. Jordan |
| 2020 | KDD | Debiasing Grid-based Product Search in E-commerce. | Ruocheng Guo, Xiaoting Zhao, Adam Henderson, Liangjie Hong, Huan Liu |
| 2020 | KDD | Tutorial on Online User Engagement: Metrics and Optimization. | Liangjie Hong, Mounia Lalmas |
| 2020 | KDD | Causal Meta-Mediation Analysis: Inferring Dose-Response Function From Summary Statistics of Many Randomized Experiments. | Zenan Wang, Xuan Yin, Tianbo Li, Liangjie Hong |
| 2020 | WWW | Attentive Sequential Models of Latent Intent for Next Item Recommendation. | Md. Mehrab Tanjim, Congzhe Su, Ethan Benjamin, Diane Hu, Liangjie Hong, Julian J. McAuley |
| 2020 | WWW | The Difference Between a Click and a Cart-Add: Learning Interaction-Specific Embeddings. | Xiaoting Zhao, Raphael Louca, Diane Hu, Liangjie Hong |
| 2020 | SIGIR | Next-item Recommendation with Sequential Hypergraphs. | Jianling Wang, Kaize Ding, Liangjie Hong, Huan Liu, James Caverlee |
| 2020 | WSDM | Time to Shop for Valentine's Day: Shopping Occasions and Sequential Recommendation in E-commerce. | Jianling Wang, Raphael Louca, Diane Hu, Caitlin Cellier, James Caverlee, Liangjie Hong |
| 2019 | KDD | Understanding the Role of Style in E-commerce Shopping. | Hao Jiang, Aakash Sabharwal, Adam Henderson, Diane Hu, Liangjie Hong |
| 2019 | KDD | The Identification and Estimation of Direct and Indirect Effects in A/B Tests through Causal Mediation Analysis. | Xuan Yin, Liangjie Hong |
| 2019 | RecSys | Joint Optimization of Profit and Relevance for Recommendation Systems in E-commerce. | Raphael Louca, Moumita Bhattacharya, Diane Hu, Liangjie Hong |
| 2019 | WWW | Tutorial on Online User Engagement: Metrics and Optimization. | Liangjie Hong, Mounia Lalmas |
| 2019 | WSDM | DAPA: The WSDM 2019 Workshop on Deep Matching in Practical Applications. | Yixing Fan, Qingyao Ai, Zhaochun Ren, Liangjie Hong, Dawei Yin, Jiafeng Guo |
| 2019 | WSDM | A Sequential Test for Selecting the Better Variant: Online A/B testing, Adaptive Allocation, and Continuous Monitoring. | Nianqiao Ju, Diane Hu, Adam Henderson, Liangjie Hong |
| 2018 | RecSys | Learning within-session budgets from browsing trajectories. | Diane Hu, Raphael Louca, Liangjie Hong, Julian J. McAuley |
| 2018 | SIGIR | Turning Clicks into Purchases: Revenue Optimization for Product Search in E-Commerce. | Liang Wu, Diane Hu, Liangjie Hong, Huan Liu |
| 2018 | WSDM | Tutorial on Metrics of User Engagement: Applications to News, Search and E-Commerce. | Mounia Lalmas, Liangjie Hong |
| 2017 | CIKM | Returning is Believing: Optimizing Long-term User Engagement in Recommender Systems. | Qingyun Wu, Hongning Wang, Liangjie Hong, Yue Shi |
| 2017 | KDD | An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy. | Kamelia Aryafar, Devin Guillory, Liangjie Hong |
| 2017 | KDD | On Sampling Strategies for Neural Network-based Collaborative Filtering. | Ting Chen, Yizhou Sun, Yue Shi, Liangjie Hong |
| 2017 | RecSys | A Gradient-based Adaptive Learning Framework for Efficient Personal Recommendation. | Yue Ning, Yue Shi, Liangjie Hong, Huzefa Rangwala, Naren Ramakrishnan |
| 2017 | WWW | GB-CENT: Gradient Boosted Categorical Embedding and Numerical Trees. | Qian Zhao, Yue Shi, Liangjie Hong |
| 2015 | CIKM | Structured Sparse Regression for Recommender Systems. | Mingjie Qian, Liangjie Hong, Yue Shi, Suju Rajan |
| 2014 | RecSys | Beyond clicks: dwell time for personalization. | Xing Yi, Liangjie Hong, Erheng Zhong, Nathan Nan Liu, Suju Rajan |
| 2013 | CIKM | The first workshop on user engagement optimization. | Liangjie Hong, Shuang-Hong Yang |
| 2013 | ICML | Nested Chinese Restaurant Franchise Process: Applications to User Tracking and Document Modeling. | Amr Ahmed, Liangjie Hong, Alexander J. Smola |
| 2013 | WWW | Hierarchical geographical modeling of user locations from social media posts. | Amr Ahmed, Liangjie Hong, Alexander J. Smola |
| 2013 | WSDM | Co-factorization machines: modeling user interests and predicting individual decisions in Twitter. | Liangjie Hong, Aziz S. Doumith, Brian D. Davison |
| 2012 | WWW | Discovering geographical topics in the twitter stream. | Liangjie Hong, Amr Ahmed, Siva Gurumurthy, Alexander J. Smola, Kostas Tsioutsiouliklis |
| 2012 | SIGIR | Learning to rank social update streams. | Liangjie Hong, Ron Bekkerman, Joseph Adler, Brian D. Davison |
| 2011 | AAAI | Temporal Dynamics of User Interests in Tagging Systems. | Dawei Yin, Liangjie Hong, Zhenzhen Xue, Brian D. Davison |
| 2011 | CIKM | Structural link analysis and prediction in microblogs. | Dawei Yin, Liangjie Hong, Brian D. Davison |
| 2011 | KDD | A time-dependent topic model for multiple text streams. | Liangjie Hong, Byron Dom, Siva Gurumurthy, Kostas Tsioutsiouliklis |
| 2011 | KDD | Tracking trends: incorporating term volume into temporal topic models. | Liangjie Hong, Dawei Yin, Jian Guo, Brian D. Davison |
| 2011 | WWW | Predicting popular messages in Twitter. | Liangjie Hong, Ovidiu Dan, Brian D. Davison |
| 2011 | WWW | Exploiting session-like behaviors in tag prediction. | Dawei Yin, Liangjie Hong, Brian D. Davison |
| 2011 | SIGIR | Link formation analysis in microblogs. | Dawei Yin, Liangjie Hong, Xiong Xiong, Brian D. Davison |
| 2010 | KDD | Empirical study of topic modeling in Twitter. | Liangjie Hong, Brian D. Davison |
| 2010 | KDD | A probabilistic model for personalized tag prediction. | Dawei Yin, Zhenzhen Xue, Liangjie Hong, Brian D. Davison |
| 2009 | SIGIR | A classification-based approach to question answering in discussion boards. | Liangjie Hong, Brian D. Davison |